{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using gpu device 0: GeForce GTX 1070 (CNMeM is enabled with initial size: 75.0% of memory, cuDNN 5005)\n"
     ]
    }
   ],
   "source": [
    "import theano\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import MDBN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding a layer with 559 input and 40 outputs\n",
      "Adding a layer with 19937 input and 400 outputs\n",
      "Adding a layer with 400 input and 40 outputs\n",
      "Adding a layer with 1686 input and 40 outputs\n",
      "Adding a layer with 120 input and 24 outputs\n",
      "Adding a layer with 24 input and 3 outputs\n"
     ]
    }
   ],
   "source": [
    "import AMLsm\n",
    "me_DBN, ge_DBN, sm_DBN, dm_DBN, top_DBN = AMLsm.load_network('Run_2016-12-21_0822/Exp_2016-12-21_0822_run_0.npz','../MDBN_run')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "datafiles = AMLsm.prepare_AML_TCGA_datafiles('../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ME_output, _ = me_DBN.MLP_output_from_datafile(datafiles['ME'], datadir='../data')\n",
    "GE_output, _ = ge_DBN.MLP_output_from_datafile(datafiles['GE'], datadir='../data')\n",
    "SM_output, _ = sm_DBN.MLP_output_from_datafile(datafiles['SM'], datadir='../data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.5, 119.5, 169.5, -0.5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NWtU6gWmIjRs39tcsofIEKoNcDp7gsoDJztMcmQ455JD+WsIIAJNKSN46Cnhi1raewPaF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\nZ0Qv4H2TllJYuyc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y+HZtwqs1DYzw29q9EcHEi4cecQzT6sbwfBK8bX2tnsLqi6OPPlq9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f067b4d6cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "joint_layer = np.concatenate([ME_output, GE_output, SM_output],axis=1)\n",
    "plt.imshow(joint_layer, cmap='gray',interpolation='none')\n",
    "plt.axis('tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f0677999850>,\n",
       "  <matplotlib.axis.XTick at 0x7f06779997d0>,\n",
       "  <matplotlib.axis.XTick at 0x7f06778f2690>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ng0i5PMWxGk53NEnzRtJeYMnMRsisZm7UykHazEZI85bBc43DFnM6yWw6pzsG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0677999ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_output = top_DBN.get_output(theano.shared(joint_layer,borrow=True))\n",
    "plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])\n",
    "plt.colorbar()\n",
    "plt.axis('tight')\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f06779b7b50>,\n",
       "  <matplotlib.axis.XTick at 0x7f067789ad50>,\n",
       "  <matplotlib.axis.XTick at 0x7f06777b8f10>],\n",
       " <a list of 3 Text xticklabel objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7U9dyhKQtgauBLfLHpRGxUNJJwAeAB/NdF0bE5flrTgQWkL07j42IpZ2Ova0v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\nLCEnYTOzhJyEzcwS+v8EVuz/zcFIZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06778d9b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(top_output, interpolation='none',extent=[0,3,385,0])\n",
    "plt.axis('tight')\n",
    "plt.colorbar()\n",
    "plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([ 76.,   2.,   2.,   1.,   0.,   2.,   3.,   1.,   5.,  78.]),\n",
       "  array([ 101.,    3.,    6.,    2.,    3.,    2.,    0.,    1.,    2.,   50.]),\n",
       "  array([ 115.,    4.,    0.,    0.,    3.,    4.,    1.,    3.,    4.,   36.])],\n",
       " array([  4.84526641e-09,   9.99999686e-02,   1.99999932e-01,\n",
       "          2.99999896e-01,   3.99999860e-01,   4.99999824e-01,\n",
       "          5.99999787e-01,   6.99999751e-01,   7.99999715e-01,\n",
       "          8.99999679e-01,   9.99999642e-01]),\n",
       " <a list of 3 Lists of Patches objects>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H47EprtI544wZzjzz3Wzbtqtrm0e/WZz5yTPZdkb3n1H/w6NcsmtX1//UY8DMY4+NVmwj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\nBr4kNcLAl6RG/B94cNAQABaJ4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f067ad09190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 115.,    4.,    0.,    0.,    3.,    4.,    1.,    3.,    4.,   36.]),\n",
       " array([  9.76478987e-09,   9.99987809e-02,   1.99997552e-01,\n",
       "          2.99996323e-01,   3.99995094e-01,   4.99993866e-01,\n",
       "          5.99992637e-01,   6.99991408e-01,   7.99990179e-01,\n",
       "          8.99988950e-01,   9.99987721e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EFuDOqnpNkucCX0zyoqr6yawLezKYdOAPcgPWfuBZyxyzGgx0M1qSFwHXAudV1dH+pXsy\nG6QvXgrcmN4daScC5yc5WFU7plTjtAzSFz8AflhVPwV+muSfgRfTm+9eTQbpi1cBfw5QVd9N8h/A\nGcDXp1LhyjFUbk56SudrwPOSnJJkLfBGYOkf7A7gtwCSvBL4cVUtTriuWVi2L5I8G/g08Naq+u4M\napyWZfuiqk7rlufQm8e/YhWGPQz2N3Iz8OokxyZ5Or0P6VbjfS2D9MU9wLkA3Zz184HvTbXK6QlH\n/s92qNyc6Ai/jnADVpLf7T1c11bV55NckOQ7wH/Tm8pYdQbpC+BPgGcCH+lGtgeratX96NyAffG4\nU6Ze5JQM+Dfy7SS3AHfR+4Gda6tqzwzLnogBXxfXANcnuZNeGP5RVT04u6onI8nHgTnghCT30ft2\n0lpGzE1vvJKkRkz8xitJ0spg4EtSIwx8SWqEgS9JjTDwJakRBr4kNcLAl6RGGPiS1Ij/A1DkFfEN\nf/syAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac09edb250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(top_output[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "code = (top_output[:,0:2] > 0.5) * np.ones_like(top_output[:,0:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from utils import find_unique_classes\n",
    "U = find_unique_classes(code)\n",
    "cl = U[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  0.,  2.,  2.,  2.,  0.,  0.,  2.,  0.,  2.,  0.,  3.,  2.,\n",
       "        2.,  3.,  3.,  2.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,\n",
       "        0.,  0.,  0.,  0.,  2.,  1.,  1.,  0.,  0.,  2.,  2.,  0.,  3.,\n",
       "        0.,  2.,  0.,  2.,  0.,  2.,  3.,  2.,  3.,  0.,  0.,  1.,  2.,\n",
       "        0.,  0.,  2.,  3.,  2.,  0.,  3.,  1.,  0.,  2.,  2.,  2.,  2.,\n",
       "        2.,  0.,  3.,  0.,  3.,  3.,  3.,  0.,  0.,  3.,  0.,  2.,  2.,\n",
       "        1.,  3.,  3.,  0.,  2.,  2.,  3.,  1.,  2.,  0.,  0.,  2.,  2.,\n",
       "        2.,  0.,  0.,  2.,  0.,  3.,  1.,  2.,  0.,  0.,  1.,  3.,  3.,\n",
       "        2.,  3.,  0.,  0.,  0.,  3.,  3.,  0.,  3.,  3.,  2.,  3.,  0.,\n",
       "        3.,  2.,  0.,  2.,  2.,  1.,  0.,  2.,  0.,  2.,  2.,  2.,  2.,\n",
       "        2.,  0.,  0.,  2.,  2.,  2.,  0.,  2.,  2.,  0.,  3.,  1.,  1.,\n",
       "        0.,  1.,  3.,  1.,  0.,  0.,  0.,  2.,  3.,  1.,  2.,  2.,  1.,\n",
       "        3.,  2.,  1.,  1.,  2.,  1.,  3.,  0.,  2.,  1.,  2.,  3.,  2.,  1.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 58.,  23.,  57.,  32.]),\n",
       " array([ 0.  ,  0.75,  1.5 ,  2.25,  3.  ]),\n",
       " <a list of 4 Patch objects>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FHbsnnbFvjYzdO4H7quptZ7h/0cdvy/4t6hgmeVaSvd3604FrWP8gxkYTj90gp1/qDBci\nJfnj9bvr7VX14SQvT/IF4DvAq4Y49qz16RvwO0n+BPgB8Bjwu/OreHJJ3gusAj+V5EvATcBTWfCx\ng/F9Y/HH7grg94AT3bnZAt7I+qe1Whi/sf1jccfw2cCRJGE9W95dVR/dbm568ZEkNcT/zk6SGmKo\nS1JDDHVJaoihLkkNMdQlqSGGuiQ1xFCXpIYY6pLUkP8DX0r690IIG5AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0677652310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cl,bins=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check Survival curves for the different classes\n",
    "==============================================="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import csv\n",
    "id=[]\n",
    "with open('../data/'+datafiles['ME']) as f:\n",
    "    my_csv = csv.reader(f,delimiter='\\t')\n",
    "    id = my_csv.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "stat={}\n",
    "with open('../data/AML/AML_clinical_data2.csv') as f:\n",
    "    reader = csv.reader(f, delimiter=',')\n",
    "    for row in reader:\n",
    "        patient_id=row[0]\n",
    "        stat[patient_id]=(row[4],row[7],row[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following case IDs were  not found in clinical data\n",
      "No data for TCGA-AB-2887\n",
      "No data for TCGA-AB-2891\n",
      "No data for TCGA-AB-2918\n",
      "No data for TCGA-AB-2921\n",
      "No data for TCGA-AB-2930\n",
      "No data for TCGA-AB-2940\n",
      "No data for TCGA-AB-2943\n",
      "No data for TCGA-AB-2946\n",
      "No data for TCGA-AB-2975\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "time_list = []\n",
    "event_list = []\n",
    "group_list = []\n",
    "print('The following case IDs were  not found in clinical data')\n",
    "for index, key in enumerate(id[1:]):\n",
    "    m = re.match('TCGA-\\w+-\\d+', key)\n",
    "    patient_id = m.group(0)\n",
    "    if patient_id in stat:\n",
    "        patient_stat = stat[patient_id]\n",
    "        add_group = True\n",
    "        try:\n",
    "            time_list.append(float(patient_stat[2]))\n",
    "            event_list.append(1)\n",
    "        except ValueError:\n",
    "            try:\n",
    "                time_list.append(float(patient_stat[1]))\n",
    "                event_list.append(0)\n",
    "            except ValueError:\n",
    "                print('No data for %s' % patient_id)\n",
    "                add_group = False\n",
    "        if add_group:\n",
    "            group_list.append(cl[index])\n",
    "    else:\n",
    "        print(patient_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0674612310>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sx8XAf4TbSW/nvcAKd/9e5FhaP9OD2priz7U6vVPC/wDbCN4Urwbm1vqN7iDb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\ngriISIIpiIuIJJiCuIhIgimIi4gk2P8HcgdpOWgfII4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0674661410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from lifelines import KaplanMeierFitter\n",
    "kmf = KaplanMeierFitter()\n",
    "kmf.fit(time_list,event_observed=event_list)\n",
    "kmf.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I59P0s/nJmo8BuCrP66j0pCkY5MYr53dtL7p1cZ5rJCJQ4kG8pn9/draGO/ua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\nRMTHFMRFRHxMQVxExMcUxEVEfOz/A3WQOFV/9hnnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06745cc150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T=np.array(time_list)\n",
    "E=np.array(event_list)\n",
    "ix = (np.array(group_list) == 0)\n",
    "kmf.fit(T[ix], E[ix], label='group 0')\n",
    "ax=kmf.plot()\n",
    "for i in range(1,4):\n",
    "    ix=(np.array(group_list)==i)\n",
    "    kmf.fit(T[ix], E[ix], label='group %d' % i)\n",
    "    kmf.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
